Abstract
This paper provides a comparative study of machine learning techniques for two-group discrimination. Simulated data is used to examine how the different learning techniques perform with respect to certain data distribution characteristics. Both linear and nonlinear discrimination methods are considered. The data has been previously used in the comparative evaluation of a number of techniques and helps relate our findings across a range of discrimination techniques.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 871-899 |
| Number of pages | 29 |
| Journal | Decision Sciences |
| Volume | 29 |
| Issue number | 4 |
| DOIs | |
| State | Published - 1998 |
All Science Journal Classification (ASJC) codes
- General Business, Management and Accounting
- Strategy and Management
- Information Systems and Management
- Management of Technology and Innovation